Split Bregman algorithms for multiple measurement vector problem
From MaRDI portal
Publication:336036
DOI10.1007/s11045-013-0251-6zbMath1349.94098OpenAlexW2081976260MaRDI QIDQ336036
Qiheng Zhang, Jian Zou, Y. L. Fu, Hai-Feng Li
Publication date: 10 November 2016
Published in: Multidimensional Systems and Signal Processing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11045-013-0251-6
Related Items (1)
Uses Software
Cites Work
- Bregman iteration algorithm for sparse nonnegative matrix factorizations via alternating \(l_1\)-norm minimization
- Fixed point and Bregman iterative methods for matrix rank minimization
- A coordinate gradient descent method for nonsmooth separable minimization
- The benefit of group sparsity
- Algorithms for simultaneous sparse approximation. I: Greedy pursuit
- Algorithms for simultaneous sparse approximation. II: Convex relaxation
- Efficient block-coordinate descent algorithms for the group Lasso
- The Split Bregman Method for L1-Regularized Problems
- Split Bregman Methods and Frame Based Image Restoration
- Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors
- Block-Sparse Signals: Uncertainty Relations and Efficient Recovery
- Distributed Spectrum Sensing for Cognitive Radio Networks by Exploiting Sparsity
- Structured Compressed Sensing: From Theory to Applications
- Theoretical Results on Sparse Representations of Multiple-Measurement Vectors
- Subspace Methods for Joint Sparse Recovery
- Rank Awareness in Joint Sparse Recovery
- Theoretical and Empirical Results for Recovery From Multiple Measurements
- Sparse solutions to linear inverse problems with multiple measurement vectors
- Model Selection and Estimation in Regression with Grouped Variables
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
This page was built for publication: Split Bregman algorithms for multiple measurement vector problem
